Identifying myself as an expat has largely shaped my personality, perspective, and aspiration. Since 2015, I have garnered extensive expertise in research, coding, mentoring, and fostering collaborations with diverse academic and industrial partners in Europe and the U.S.

This multifaceted experience allowed me to learn a robust problem-solving approach to face challenges at work, including three fundamental steps: understanding and visualizing problems quickly, developing and implementing innovative solutions, and evaluating and optimizing the outcomes. This strategy has been pivotal in my professional success and personal development. I continuously expanded my knowledge and stayed up-to-date with the latest advancements in computer science, biomedical engineering, and digital health.

My background is in digital health and artificial intelligence, and my mission is to address challenges in this field, from multimodal data collection to extracting actionable insights and delivering personalized interventions, by leveraging artificial intelligence, health data processing, and wearable Internet-of-Things technologies. My expertise spans designing data analysis methods – such as signal processing, machine learning, and large language models – in mobile health and conversational health applications.

My job responsibilities have included the following:

  • Research: I have built theoretical knowledge across a wide range of topics, such as Artificial Intelligence, Internet of Things, health data analytics, large language models, wearable electronics, fog computing, and digital health. My current focus is on LLM-based agents, agent evaluation, and safety guardrails for production health applications. My broader work has covered data analysis methods for biomedical signals, e.g., photoplethysmogram (PPG) and electrocardiogram (ECG); personalized machine-learning-based health data analytics (e.g., CNN-, LSTM-, and GAN-based); the application of LLMs in healthcare; anomaly event detection methods for longitudinal and multivariate data; and signal quality assessment, to mention a few. I am the co-creator of openCHA, an open-source LLM-powered framework that enables Conversational Health Agents (CHAs) to deliver effective healthcare services, including patient support, health coaching, and promoting patient self-awareness. I am also the co-creator of e2epyppg, an end-to-end PPG signal processing pipeline. I have been collaborating with multidisciplinary teams at Thrive AI Health, the University of California Irvine, Stanford University, iHealth Labs, University of Turku, Vienna University of Technology, and Silo AI, to mention a few. I am the author of more than 80 peer-reviewed publications, both in medical and technological venues.
  • Coding: I have built my coding skills (mostly in Python since 2015) by taking an active role in different projects including health data collection (e.g., PPG and ECG) and wearable- and cloud-based health data analysis. I gained experience working with various Python libraries, including TensorFlow, Scikit-learn, SciPy, and Pandas.
  • Mentoring: I have served as an advisor to master’s and Ph.D. students working on AI solutions for healthcare applications. My primary objective is to mentor students and junior researchers, helping them strengthen their expertise in signal processing and machine learning, and improve their critical thinking and problem-solving abilities.
  • Teaching: I was the teacher of two courses, “Acquisition and Analysis of Biosignals” and “Biosignal Analytics,” for three years, where I taught the fundamentals of signal processing and machine learning used for biomedical signals and health data analysis.

Research Area & Interests

  • mHealth
  • LLM Agents
  • Wearable-based Health Monitoring and Coaching
  • Machine Learning
  • Biomedical signal Processing